Choose the intelligence route that fits the work.
QUI separates agent infrastructure from model dependency, giving companies governed choices across cloud, local, and private inference routes.
The Problem
Different work has different requirements.
Some tasks need the strongest available cloud model. Some need predictable cost. Some need low latency. Some should remain local. Some should run on private company-controlled compute. Some require a specific provider for policy, contract, or capability reasons.
Own your intelligence layer and dynamically route work to the cheapest model capable of doing the job.
A serious AI platform cannot assume one route fits every workload.
QUI's Routing Options
Managed Cloud
Use supported cloud model providers through governed routing and billing controls.
Local Qllama
Run supported local models on company hardware when cloud inference is not the right route.
Cloud Private
Route to owner-registered private compute when the company wants remote inference without using the managed cloud proxy path.
Multi-Provider Support
QUI is designed around multiple providers, including OpenAI, Anthropic, Google, and X/Grok.
Cost-aware routing helps teams reserve premium models for work that needs them and use cheaper capable models for routine tasks.
What Stays Constant
The agent remains defined inside QUI.
Its identity, memory, prompts, tools, workflows, and governance do not have to be rebuilt around one provider. The route can change while the company-owned intelligence layer remains intact.
Business Benefit
Model and deployment choice gives companies leverage.
They can optimize for:
- Privacy
- Cost
- Token efficiency
- Capability
- Latency
- Availability
- Vendor strategy
- Workload sensitivity
- Compliance requirements